Human-powered vehicle control device, learning method, human-powered vehicle control method and computer program

ABSTRACT

A human-powered vehicle control device includes at least one sensor, a memory, a controller and an interpolation processor. The at least one sensor is configured to acquire input information related to traveling of a human-powered vehicle. The memory is configured to store a first learning model trained so as to output output information related to control of a device mounted on the human-powered vehicle based on the input information acquired. The controller is configured to control the device by control data decided based on output information obtained by inputting the input information to the first learning model. The interpolation processor is configured to execute processing of interpolating the first learning model in the memory using a second learning model trained with input information in a human-powered vehicle different in at least one of the human-powered vehicle and a rider of the human-powered vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Japanese Patent Application No.2022-060934, filed on Mar. 31, 2022. The entire disclosure of JapanesePatent Application No. 2022-060934 is hereby incorporated herein byreference.

BACKGROUND Technical Field

The present invention generally relates to a human-powered vehiclecontrol device, a learning method, a human-powered vehicle controlmethod and a computer program.

Background Information

As electrification of human-powered vehicles has recently beenincreasing, automatic control of its mounted device including atransmission device has been achieved. A model has been proposed that istrained by deep learning so as to output information on control of themounted devices in the case where information obtained if informationacquired from at least one of a speed sensor, a cadence sensor, a torquesensor and a camera provided in the human-powered vehicle is input(Japanese Patent No. 6985217—Patent Document 1).

SUMMARY

By achieving automatic control of the human-powered vehicle with deeplearning, automatic control on an individual rider basis can beoptimized by taking the physical characteristics of the rider andpreferences of the rider into account.

It is an object of the present disclosure to provide a human-poweredvehicle control device, a learning method, a human-powered vehiclecontrol method and a computer program that reduce the time required totrain a model used for automatic control and achieve automatic controleven in an untrained situation.

A human-powered vehicle control device according to the first aspect ofthe present disclosure comprises: at least one sensor, a memory, anelectronic controller and an interpolation processor. The at least onesensor is configured to acquire input information related to travelingof a human-powered vehicle. The a memory is configured to store a firstlearning model trained so as to output output information related tocontrol of a device mounted on the human-powered vehicle based on theinput information acquired. The electronic controller is configured tocontrol the device by control data decided based on output informationobtained by inputting the input information to the first learning model.The interpolation processor is configured to execute processing ofinterpolating the first learning model stored in the memory using asecond learning model trained with input information in a human-poweredvehicle different in at least one of the human-powered vehicle and arider of the human-powered vehicle.

According to the human-powered vehicle control device of theabove-mentioned first aspect, the first learning model used for controlof a device is interpolated by using the second learning model, which isdifferent from the first learning model. In the case where learning isperformed based on at least one of the human-powered vehicle and therider individually, a situation in which the human-powered vehicle hasnot traveled is not learned. Interpolation of the first learning modelusing the second learning model reduces the time for learning inputinformation in the unlearned traveling situation and enables automaticcontrol itself.

For the human-powered vehicle control device according to a secondaspect of the present disclosure, in the human-powered vehicle controldevice according to the above-mentioned first aspect, the interpolationprocessor is configured to update at least part of the first learningmodel stored in the memory with the second learning model.

According to the human-powered vehicle control device of theabove-mentioned second aspect, interpolation using the second learningmodel reduces the time for learning input information in the unlearnedtraveling situation and enables automatic control itself.

For the human-powered vehicle control device according to a third aspectof the present disclosure, in the human-powered vehicle control deviceaccording to the above-mentioned first aspect, the interpolationprocessor is configured to train the first learning model using, astraining data, input information acquired by the at least one sensor andoutput information output if the input information is input to thesecond learning model.

According to the human-powered vehicle control device of theabove-mentioned third aspect, interpolation through learning using theoutput information output from the second learning model reduces thetime for learning input information in the unlearned traveling situationand enables automatic control itself.

For the human-powered vehicle control device according to a fourthaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to thirdaspects, the first learning model is trained with input informationacquired by the at least one sensor in a plurality of travelingsituations different from each other, and as to a traveling situationthat is an unlearned traveling situation different from a learnedtraveling situation learned by the first learning model and that islearned by a second learning model, the interpolation processor isconfigured to interpolate the first learning model using the secondlearning model.

According to the human-powered vehicle control device of theabove-mentioned fourth aspect, interpolation is performed for eachtraveling situation. This reduces the time for learning inputinformation in the unlearned traveling situation and enables automaticcontrol itself.

For the human-powered vehicle control device according to a fifth aspectof the present disclosure, in the human-powered vehicle control deviceaccording to any one of the above-mentioned first to third aspects, thefirst learning model includes a plurality of learning models stored foreach traveling situation, and the interpolation processor is configuredto use, as a learning model corresponding to an unlearned travelingsituation different from a learned traveling situation learned by thefirst learning model out of the plurality of learning models, a part ofthe second learning model that has already learned the unlearnedtraveling situation.

According to the human-powered vehicle control device of theabove-mentioned fifth aspect, the learning model for the unlearnedsituation is stored as the learning model for the first learning model,which reduces the time for learning input information in the unlearnedtraveling situation and enables automatic control itself.

For the human-powered vehicle control device according to a sixth aspectof the present disclosure, in the human-powered vehicle control deviceaccording to the above-mentioned fourth or fifth aspect, the travelingsituation is distinguished by at least one of on-road, off-road andurban district situations.

According to the human-powered vehicle control device of theabove-mentioned sixth aspect, interpolation using the second learningmodel reduces the time for learning input information in the unlearnedtraveling situation out of the on-road, off-road and urban districtsituations and enables automatic control itself.

For the human-powered vehicle control device according to a seventhaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned fourth to sixthaspects, the traveling situation is distinguished by at least one ofuphill, flat and downhill situations.

According to the human-powered vehicle control device of theabove-mentioned seventh aspect, interpolation using the second learningmodel reduces the time for learning input information in the unlearnedtraveling situation out of the uphill, flat and downhill situations, andenables automatic control itself.

For the human-powered vehicle control device according to an eighthaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to seventhaspects, the interpolation processor is configured to use the secondlearning model for which output information output if the same inputinformation is input is similar to output information output if theinput information is input to the first learning model.

According to the human-powered vehicle control device of theabove-mentioned eighth aspect, interpolation using the similar secondlearning model reduces the time for learning input information in theunlearned traveling situation and enables automatic control itself.

For the human-powered vehicle control device according to a ninth aspectof the present disclosure, in the human-powered vehicle control deviceaccording to the above-mentioned eighth aspect, the first learning modelis trained with input information acquired by the at least one sensorfor a plurality of traveling situations different from each other, andout of a plurality of the second learning models, the interpolationprocessor is configured to use the second learning model for whichoutput information output if input information in a traveling situationalready learned by the first learning model is input is similar tooutput information output if the input information is input to the firstlearning model.

According to the human-powered vehicle control device of theabove-mentioned ninth aspect, interpolation using the similar secondlearning model reduces the time for learning input information in theunlearned traveling situation out of the on-road, off-road and urbandistrict situations and enables automatic control itself.

For the human-powered vehicle control device according to a tenth aspectof the present disclosure, in the human-powered vehicle control deviceaccording to the above-mentioned eighth aspect, the first learning modeland the second learning model each include a plurality of learningmodels stored for each traveling situation. The interpolation processoris configured to use, out of a plurality of the second learning models,the second learning model for which output information output if inputinformation in a traveling situation already learned by the firstlearning model is input is similar to output information output if theinput information is input to the first learning model. Theinterpolation processor is configured to acquire, out of the pluralityof learning models included in the second learning model, the learningmodel that corresponds to an unlearned traveling situation differentfrom the learned traveling situation.

According to the human-powered vehicle control device of theabove-mentioned tenth aspect, interpolation using the learning modelincluded in the second learning model as a learning model for the firstlearning model reduces the time for learning input information in theunlearned traveling situation and enables automatic control itself.

For the human-powered vehicle control device according to an eleventhaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to seventhaspects, the interpolation processor is configured to use a secondlearning model used in another human-powered vehicle control devicesimilar in control data decided based on the same input information.

According to the human-powered vehicle control device of theabove-mentioned eleventh aspect, interpolation using the second learningmodel that outputs similar control data reduces the time for learninginput information in the unlearned traveling situation and enablesautomatic control itself.

For the human-powered vehicle control device according to a twelfthaspect of the present disclosure, in the human-powered vehicle controldevice according to the above-mentioned eleventh aspect, the firstlearning model is trained with input information acquired by the atleast one sensor in a plurality of traveling situations different fromeach other, and the interpolation processor is configured to use, out ofa plurality of the second learning models, the second learning model forwhich control data decided based on output information output if inputinformation in a traveling situation already learned by the firstlearning model is input is similar to control data decided based onoutput information output if the input information is input to the firstlearning model.

According to the human-powered vehicle control device of theabove-mentioned twelfth aspect, interpolation using the second learningmodel that outputs similar control data reduces the time for learninginput information in the unlearned traveling situation and enablesautomatic control itself.

For the human-powered vehicle control device according to a thirteenthaspect of the present disclosure, in the human-powered vehicle controldevice according to the above-mentioned eleventh aspect, the firstlearning model and the second learning model each include a plurality oflearning models stored for each traveling situation. The interpolationprocessor is configured to use, out of a plurality of the secondlearning models, the second learning model for which control datadecided based on output information output if input information in atraveling situation already learned by the first learning model is inputis similar to control data decided based on output information output ifthe input information is input to the first learning model. Theinterpolation processor is configured to acquire, out of the pluralityof learning models included in the second learning model, the learningmodel corresponding to an unlearned traveling situation different fromthe learned traveling situation.

According to the human-powered vehicle control device of theabove-mentioned thirteenth aspect, interpolation performed by storing,as a learning model for the first learning model, the learning modelincluded in the second learning model that outputs similar control datareduces the time for learning input information for the unlearnedtraveling situation and enables automatic control itself.

For the human-powered vehicle control device according to a fourteenthaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to seventhaspects, the interpolation processor is configured to use, out of aplurality of the second learning models, the second learning model thatis trained with input information in another human-powered vehicle thesame as or similar to, in at least one of type and size, thehuman-powered vehicle.

According to the human-powered vehicle control device of theabove-mentioned fourteenth aspect, interpolation using the secondlearning model of a human-powered vehicle similar in type and size ofthe human-powered vehicle reduces the time for learning inputinformation in the unlearned traveling situation and enables automaticcontrol itself.

For the human-powered vehicle control device according to a fifteenthaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to seventhaspects, the interpolation processor is configured to use, out of aplurality of the second learning models, the second learning model thatis trained with input information of another human-powered vehiclemounted with a device having the same type and manufacture as or similartype and manufacturer to the device.

According to the human-powered vehicle control device of theabove-mentioned fifteenth aspect, interpolation using a similar secondlearning model of a human-powered vehicle mounted with a device havingthe same as or similar to the device of the human-powered vehiclereduces the time for learning input information in the unlearnedtraveling situation and enables automatic control itself.

For the human-powered vehicle control device according to a sixteenthaspect of the present disclosure, in the human-powered vehicle controldevice according to the above-mentioned fifteenth aspect, the device isdistinguished by at least one type of a transmission device, asuspension, a seat post, a brake device and an assist device.

According to the human-powered vehicle control device of theabove-mentioned sixteenth aspect, interpolation using a second learningmodel of a human-powered vehicle mounted with the transmission device,suspension, seat post, brake device and assist device the same as orsimilar to those mounted on the human-powered vehicle reduces the timefor learning input information in the unlearned traveling situation andenables automatic control itself.

For the human-powered vehicle control device according to a seventeenthaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to seventhaspects, the interpolation processor is configured to use, out of aplurality of the second learning models, the second learning model thatis trained with input information for a human-powered vehicle of a riderof the same type as or similar type to a rider of the human-poweredvehicle.

According to the human-powered vehicle control device of theabove-mentioned seventeenth aspect, interpolation using the secondlearning model trained in a human-powered vehicle of a rider of the sametype as or similar type to a rider of the human-powered vehicle reducesthe time for learning input information in the unlearned travelingsituation and enables automatic control itself.

For the human-powered vehicle control device according to an eighteenthaspect of the present disclosure, in the human-powered vehicle controldevice according to any one of the above-mentioned first to seventeenthaspects, the first learning model stored in the memory is sent toanother device.

According to the human-powered vehicle control device of theabove-mentioned eighteenth aspect, the first learning model trained ineach human-powered vehicle is sent and available for interpolation ofthe first learning model of another human-powered vehicle.

For the human-powered vehicle control device of a nineteenth aspect ofthe present disclosure, in the human-powered vehicle control deviceaccording to any one of the above-mentioned first to eighteenth aspects,the second learning model is a model acquired by performing staticprocessing on parameters including at least one of weights and biases ofa plurality of models trained in a plurality of other human-poweredvehicles.

According to the human-powered vehicle control device of theabove-mentioned nineteenth aspect, a model acquired by performingstatistic processing on parameters including at least one of the weightsand biases of the first learning model trained in each human-poweredvehicle is available for interpolation of the first learning model inanother human-powered vehicle.

A learning method according to a twenty aspect of the present disclosurecauses a computer mounted on a human-powered vehicle to executeprocessing of: selecting externally, as to a first learning modeltrained so as to output output information related to control of adevice mounted on the human-powered vehicle based on input informationrelated to traveling of the human-powered vehicle, a second learningmodel trained with input information in a human-powered vehicledifferent in at least one of the human-powered vehicle and a rider ofthe human-powered vehicle; and performing interpolation processing ofthe first learning model using the second learning model selected.

According to the learning method according to the above-mentionedtwentieth aspect, interpolation of the first learning model using thesecond learning model reduces the time for learning input information inthe unlearned traveling situation and enables automatic control itself.

A human-powered vehicle control method according to the above-mentionedtwenty-first aspect causes a computer mounted on a human-powered vehicleto execute the processing of: selecting externally, as to a firstlearning model trained so as to output output information related tocontrol of a device mounted on the human-powered vehicle based on inputinformation related to traveling of the human-powered vehicle, a secondlearning model trained with input information in a human-powered vehicledifferent in at least one of the human-powered vehicle and a rider ofthe human-powered vehicle; performing interpolation processing of thefirst learning model using the second learning model selected; decidingcontrol data based on output information obtained by inputting the inputinformation to the first learning model interpolated; and controllingthe device with the control data decided.

According to the human-powered vehicle control method of theabove-mentioned twenty-first aspect, interpolation of the first leaningmodel using the second learning model reduces the time for learninginput information in the unlearned traveling situation and enablesautomatic control itself.

A computer program according to a twenty-second aspect of the presentdisclosure is disposed upon a non-transitory computer readable storagemedium and executable by a computer mounted on a human-powered vehicle.The computer program is configured to cause the computer to execute theprocessing of: selecting externally, as to a first learning modeltrained so as to output output information related to control of adevice mounted on the human-powered vehicle based on input informationrelated to traveling of the human-powered vehicle, a second learningmodel trained with input information in a human-powered vehicledifferent in at least one of the human-powered vehicle and a rider ofthe human-powered vehicle; and performing interpolation processing ofthe first learning model using the second learning model selected.

According to the computer program of the above-mentioned twenty-secondaspect, interpolation of the first leaning model using the secondlearning model can reduces the time for learning input information inthe unlearned traveling situation and enables automatic control itself.

The human-powered vehicle control device, learning method, human-poweredvehicle control method and computer program according to the presentdisclosure can reduce the time required to train a model used forautomatic control and achieve automatic control even in the unlearnedsituation.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the attached drawings which form a part of thisoriginal disclosure.

FIG. 1 is a side elevational view of a human-powered vehicle to which acontrol device is applied according to a first embodiment.

FIG. 2 is a block diagram illustrating the configuration of the controldevice.

FIG. 3 illustrates one example of a first learning model.

FIG. 4 illustrates another example of the first learning model.

FIG. 5 illustrates the control device and an information processingdevice according to the first embodiment.

FIG. 6 is a block diagram illustrating the configuration of theinformation processing device.

FIG. 7 is a flowchart showing one example of a learning method of thefirst learning model according to the first embodiment.

FIG. 8 is a flowchart showing one example of control processing usingthe first learning model according to the first embodiment.

FIG. 9 is a block diagram illustrating the configuration of a controldevice according to a second embodiment.

FIG. 10 is a flowchart showing one example of a learning method of thefirst learning model according to a third embodiment.

FIG. 11 is a schematic diagram of the first learning model according toa fourth embodiment.

FIG. 12 is a flowchart showing one example of a learning method of thefirst learning model according to the fourth embodiment.

FIG. 13 is a flowchart showing one example of the learning method of thefirst learning model according to the fourth embodiment.

FIG. 14 is a flowchart showing one example of control processing of thefirst learning model according to the fourth embodiment.

FIG. 15 illustrates the first learning model according to a fifthembodiment.

FIG. 16 is a flowchart showing one example of a learning method of thefirst learning model according to the fifth embodiment.

FIG. 17 is a flowchart showing one example of the learning method of thefirst learning model according to the fifth embodiment.

FIG. 18 illustrates processing performed by an interpolation processoraccording to the fifth embodiment.

FIG. 19 is a flowchart showing one example of control processing usingthe first learning model according to the fifth embodiment.

FIG. 20 illustrates a control device and an information processingdevice according to a sixth embodiment.

FIG. 21 is a flowchart of an example of a processing procedure of theinformation processing device according to the sixth embodiment.

FIG. 22 is a schematic diagram of a second learning model according tothe sixth embodiment.

DESCRIPTION OF EMBODIMENTS

The descriptions of the embodiments below are examples of forms that ahuman-powered vehicle control device, a learning method, a human-poweredvehicle control method and a computer program according to the presentdisclosure can take, and there is no intention to limit the forms. Thehuman-powered vehicle control device, learning method, human-poweredvehicle control method and computer program according to the presentdisclosure can take forms different from the embodiments, such as formsof modification of the embodiments and a combination of at least twomodifications that do not contradict each other.

In the following description of each of the embodiments, the termsindicating direction, such as front, back, forward, backward, left,right, sideways, upper, lower and so on are used with reference to thedirections seen as the user sits in the saddle of a human-poweredvehicle.

In each of the following embodiments, the human-powered vehicle controldevice according to the present disclosure is referred to as a controldevice and described below.

First Embodiment

FIG. 1 is a side elevational view of a human-powered vehicle 1 to whicha control device 100 is applied according to a first embodiment. Thehuman-powered vehicle 1 is a vehicle that at least partially uses manpower as a driving force for traveling. Vehicles using only an internalcombustion engine or an electric motor as a driving force are excludedfrom the human-powered vehicle 1 according to the present embodiment.The human-powered vehicle 1 is a bicycle including, for example, amountain bicycle, a road bicycle, a cross bicycle, a city cycle and anelectric assisted bicycle (e-bike).

The human-powered vehicle 1 is provided with a vehicle main body 10, ahandlebar 12, a front wheel 14, a rear wheel 16 and a saddle 18. Thehuman-powered vehicle 1 is provided with a driving mechanism 20, adevice 30, an operation device 40, a battery 50 and a sensor 60.

The vehicle main body 10 is provided with a frame 10A and a front fork10B. The front wheel 14 is rotatably supported to the front fork 10B.The rear wheel 16 is rotatably supported to the frame 10A. The handlebar12 is supported to the frame 10A so as to be able to change thedirection of proceeding of the front wheel 14.

The driving mechanism 20 includes a crank 21, a first sprocket assembly23, a second sprocket assembly 25, a chain 27 and a pair of pedals 29.

The crank 21 includes a crank shaft 21A, a right crank 21B and a leftcrank 21C. The crank shaft 21A is rotatably supported to the frame 10A.The right crank 21B and the left crank 21C are coupled to the crankshaft 21A. One of the pair of pedals 29 is rotatably supported to theright crank 21B. The other one of the pair of pedals 29 is rotatablysupported to the left crank 21C.

The first sprocket assembly 23 is coupled to the crank shaft 21A so asto be rotatable as one piece. The first sprocket assembly 23 includesone or more sprockets 23A. The first sprocket assembly 23 includes themultiple sprockets 23A different in outer diameters as one example.

The second sprocket assembly 25 is rotatably coupled to a rear hub ofthe rear wheel 16. The second sprocket assembly 25 includes one or moresprockets 25A. The second sprocket assembly 25 includes the multiplesprockets 25A different in outer diameters as one example.

The chain 27 is entrained about any of the sprockets 23A of the firstsprocket assembly 23 and any of the sprockets 25A of the second sprocketassembly 25. When the crank 21 rotates forwardly by a human-powereddriving force applied to the pedals 29, the sprocket 23A rotatesforwardly together with the crank 21. The rotation of the sprocket 23Ais transmitted to the sprocket of the second sprocket assembly 25 viathe chain 27. The rotation of the sprockets 25A rotates the rear wheel16. A belt or a shaft can be employed instead of the chain 27.

The control device 100 is mounted on the battery 50, a cycle computer, adrive unit or the like of the human-powered vehicle 1 as one example.The control device 100 is connected to the device 30, the operationdevice 40 and the battery 50. The connected manner and the details ofthe control device 100 will be described later.

The human-powered vehicle 1 is provided with the device 30 that isoperated by power supplied from the battery 50 and is controlled in itsoperation by the control device 100. The device 30 includes atransmission device 31, a suspension 33, a seat post 35, a brake device37 and an assist device 39. The device 30 is basically operated throughcontrol by the control device 100 according to an operation through theoperation device 40. The target to be controlled of the control device100 corresponds to at least one of the device 30, the transmissiondevice 31, the suspension 33, the seat post 35, the brake device 37 andthe assist device 39.

The transmission device 31 changes a ratio of the rotational speed ofthe rear wheel 16 to the rotational speed of the crank 21, i.e., thegear ratio of the human-powered vehicle 1. The gear ratio is expressedas a ratio of the output rotational speed output from the transmissiondevice 31 to the input rotational speed input to the transmission device31. The gear ratio is expressed by the following formula: “gearratio=output rotational speed/input rotational speed.” As a firstexample, the transmission device 31 is an external transmission (rearderailleur) for shifting a coupled state between the second sprocketassembly 25 and the chain 27. As a second example, the transmissiondevice 31 is an external transmission (front derailleur) for shifting acoupled state between the first sprocket assembly 23 and the chain 27.As a third example, it is an internal transmission disposed at a hub ofthe rear wheel 16. The transmission device 31 can be an infinitelyvariable transmission.

The suspension device 33 is a front suspension that is disposed at thefront fork 10B and is for damping the shock applied to the front wheel14 as one example. The suspension 33 can be a rear suspension that isdisposed at the frame 10A and is for damping the shock applied to therear wheel 16 in another example. The suspension 33 includes a motor andcan control the motor by rotating or locking the motor according tocontrol data including a damping ratio, a stroke amount and whether tochange to a lockout state. The suspension 33 includes a valve forcontrolling the flow path for an internal oil or a solenoid valve andcan be controlled by control data including a damping ratio, a strokeamount and whether to change to a lockout state.

The seat post 35 is attached to the frame 10A. The seat post 35 includesa motor. The seat post 35 includes a motor and raises or lowers thesaddle 18 relative to the frame 10A. The seat post 35 can be controlledby rotating the motor with control data including a support position.

The brake device 37 includes a front brake device 371 configured tobrake the front wheel 14 and a rear brake device 372 configured to brakethe rear wheel 16. The front brake device 371 and the rear brake device372 each include a caliper brake device, a disk brake device, or thelike. The front brake device 371 and the rear brake device 372 eachinclude a motor or the like that activates a caliper brake device or adisk brake device, and can change a braking force.

The assist device 39 assists the human driving force for thehuman-powered vehicle 1. As one example, the assist device 39 isdisposed inside a drive unit. The assist device 39 is, as one example,disposed at the battery 50. The assist device 39 includes a motor. Theassist device 39 is, as one example, disposed between the crank shaft21A and the frame 10A and transmits torque to the first sprocketassembly 23 to thereby assist the human-powered driving force to thehuman-powered vehicle 1. The assist device 39, as one example, drivesthe chain 27 for transmitting a driving force to the rear wheel 16 ofthe human-powered vehicle 1 to thereby assist the human-powered drivingforce to the human-powered vehicle 1.

The operation device 40 is disposed at the handlebar 12. The operationdevice 33 includes one or more user operated members. The user operatedmembers are not limited to those illustrated FIG. 1 , and can include,for example, a button, a switch, a lever, a dial and/or a touch screen.Here, as seen in FIG. 1 , the operation device 40 includes at least oneoperation member 40A to be operated by the rider. The operation member40A includes one or more buttons. The operation member 40A includes oneor more brake levers. Here, the operation device 40 includes a pair ofdual brake-shift levers as the operation members 40A, which are providedat left and right sides of the handlebar 12. The operation members 40Aare operable by moving the brake levers sideways towards a center planeof the human-powered vehicle 1 for performing a shifting operation. Theoperation members 33A (the dual brake-shift levers) can also be pivotedin a rearward direction for performing a braking operation. Theinformation terminal device 8 held by the rider can be used as theoperation member 40A.

The operation device 40 includes a pair of transmission designatingmembers 40B. As one example, the transmission designating members 40Bcorrespond to multiple buttons that are provided to the operation member40A. As another example, the transmission designating members 40B aredevices attached to the brake levers. Every time the rider performs theoperation of moving one of the brake levers or pressing a button amongthe plurality of buttons on the transmission designating member 40B,he/she can perform manual operation on the transmission device 31 toincrease the gear ratio or decrease the gear ratio.

The operation device 40 includes a suspension designating member 40C.The suspension designating member 40C corresponds to at least one buttonprovided to the operation unit 40A, for example. By pressing thebutton(s) corresponding to the suspension designating member 40C,control data such as damping rate and stroke of the suspension can beset.

The operation device 40 includes a seat post designating member 40D. Theseat post designating member 40D corresponds to at least one buttonprovided to the operation member 40A, for example. The saddle 351 can beraised or lowered by pressing the button(s) corresponding to the seatpost designating member 40D.

The operation device 40 includes a braking designating member 40E. Thebraking designating member 40E includes one or more brake levers.Operation of the brake levers allow activation of a caliper brake systemor a disc brake system of the brake devices 37.

The operation device 40 includes an assist designating member 40F. Theassist designating member 40F corresponds to at least one buttonprovided to the operation member 40A, for example. By pressing thebutton(s) corresponding to the assist designating member 40F, the assistmode can be set to any one of multiple stages (high/mean/low).

The operation device 40 is communicably connected to the control device100 so as to send a signal corresponding to an operation to the controldevice 100. The operation device 40 can communicably be connected to thetransmission unit 31, the suspension 33, the seat post 35, the brakedevice 37 and the assist device 39 so as to send thereto a signal inresponse to an operation. As a first example, the operation device 40communicates with the control device 100 through a communication line oran electric wire that allows for Power Line Communication (PLC). Theoperation device 40 can communicate with the transmission device 31, thesuspension 33, the seat post 35, the brake device 37, the assist device39 and the control device 100 through a communication line or anelectric wire that allows for PLC. As a second example, the operationdevice 40 wirelessly communicates with the control device 100. Theoperation device 40 can wirelessly communicate with the transmissiondevice 31, the suspension 33, the seat post 35, the brake device 37, theassist device 39 and the control device 100.

The operation device 40 can be provided with a notification device thatgenerates a notification of an operating state. The operation device 40can notify a control state for the transmission device 31, thesuspension 33, the seat post 35, the brake device 37 and the assistdevice 39 to the rider with a lamp, a display, a speaker or the like.

The battery 50 includes a battery main body 51 and a battery holder 53.The battery main body 51 is a rechargeable battery including one or morebattery cells. The battery holder 53 is fixed at the frame 10A of thehuman-powered vehicle 1. The battery main body 51 is attachable to anddetachable from the battery holder 53. The battery 50 is electricallyconnected to the device 30, the operation device 40 and the controldevice 100 to supply power to them as necessary. The battery 50preferably includes an electronic controller for communicating with thecontrol device 100. The electronic controller preferably includes aprocessor employing a CPU.

The human-powered vehicle 1 is provided with the sensor 60 at each sitefor acquiring information related to traveling including a state of therider and a traveling environment. The term “sensor” as used hereinrefers to a hardware device or instrument designed to detect thepresence or absence of a particular event, object, substance, or achange in its environment, and to emit a signal in response. The term“sensor” as used herein does not include a human being. The sensor 60includes a speed sensor 61, an acceleration sensor 62, a torque sensor63, a cadence sensor 64, a gyro sensor 65, a seating sensor 66, a camera67 and a position information sensor 68.

The speed sensor 61 is disposed at the front wheel 14, for example, andtransmits to the control device 100 a signal corresponding to the numberof rotations per unit time of the front wheel 14. The control device 100can calculate a vehicle speed and a travel distance for thehuman-powered vehicle 1 based on the output of the speed sensor 61.

The acceleration sensor 62 is secured at the frame 10A, for example. Theacceleration sensor 62 is a sensor for outputting vibrations of thehuman-powered vehicle 1 in three-axes (front-back direction, right-leftdirection and up-down direction) relative to the frame 10A and isdisposed for detecting a movement and a vibration of the human-poweredvehicle 1. The acceleration sensor 62 transmits signals corresponding tothe magnitude of the movement and vibrations to the control device 100.

The torque sensor 63 is disposed so as to measure respective torquesapplied to the right crank 21B and the left crank 21C, for example. Thetorque sensor 63 outputs a signal corresponding to the torque measuredat least one of the right crank 21B and the left crank 21C to thecontrol device 100.

The cadence sensor 64 is disposed so as to measure a cadence of any oneof the right crank 21B and the left crank 21C, for example. The cadencesensor 64 transmits a signal corresponding to the measured cadence tothe control device 100.

The gyro sensor 65 is secured at the frame 10A, for example. The gyrosensor 65 is disposed so as to detect yaw, roll and pitch rotations ofthe human-powered vehicle 1. The gyro sensor 65 transmits signalscorresponding to the respective rotation amounts in the three axes tothe control device 100. Yaw is a rotation about the vertical axis. Rollis a rotation about the forward-backward axis. Pitch is a rotation aboutthe left-right axis.

The seating sensor 66 is disposed on the internal surface of the saddle351 so as to measure whether or not the rider is seated in the saddle351. The seating sensor 66 employs a piezoelectric sensor, for exampleand transmits a signal corresponding to the weight applied to the saddle351 to the control device 100.

The camera 67 is mounted on the front fork 10B so as to face the front.As a first example, the camera 67 is mounted on the front fork 10B so asto face the front together with a light. As a second example, the camera67 is provided at the handlebar 12. The camera 67 outputs videocorresponding to the user's field of vision by using a camera module.The camera 67 outputs a video signal obtained by photographing an objectpresent in the direction of travel.

The position information sensor 68 is secured at the frame 10A, forexample. The position information sensor 68 is disposed for detectinginformation on the position of the human-powered vehicle 1. For example,the position information sensor 68 is disposed for detecting informationon the terrestrial longitude and latitude of the human-powered vehicle1. For example, the position information sensor 68 is a GPS sensor. Theposition information sensor 68 transmits signals corresponding to thepositions of the human-powered vehicle 1 to the control device 100.

As needed and/or desired, the sensor 60 does not include all of thespeed sensor 61, the acceleration sensor 62, the torque sensor 63, thecadence sensor 64, the gyro sensor 65, the seating sensor 66, the camera67 and the position information sensor 68.

FIG. 2 is a block diagram illustrating the configuration of the controldevice 100. The control device 100 includes an electronic controller 110and a memory 112. The electronic controller 110 is preferably amicrocomputer that includes one or more processors. The electroniccontroller 110 is formed of one or more semiconductor chips that aremounted on a printed circuit board. The terms “controller” and“electronic controller” as used herein refer to hardware that executes asoftware program, and does not include a human being. The electroniccontroller 110 can also be simply referred to as the controller 110. Thememory 112 is any computer storage device or any non-transitorycomputer-readable medium with the sole exception of a transitory,propagating signal. In other words, the terms “memory” and “storage” asused herein refer to a non-transitory computer readable memory or anon-transitory computer readable storage. The memory 112 includes anon-volatile memory such as a flash memory, a hard disk, a ROM (ReadOnly Memory) device, and so on, for example. Also, for example, thememory 112 can also include volatile memory such as a RAM (Random AccessMemory) device. The memory 112 can also be referred to as computerstorage device 112.

The electronic controller 110 includes at least one processor employinga CPU. The electronic controller 110 can include a memory such as abuilt-in ROM (Read Only Memory), RAM (Random Access Memory) or the like.The electronic controller 110 executes processing by separatingfunctions between a device controller 116 and an interpolation processor118. The device controller 116 and the interpolation processor 118 canshare the processor of the electronic controller 110, or each of thedevice controller 116 and the interpolation processor 118 can have itsown processor. Here, the device controller 116 includes a first controlcircuit and the interpolation processor 118 includes a second controlcircuit, where the processor of the electronic controller 110 is sharedbetween the first control circuit and the second control circuit.

The device controller 116 is configured to acquire input informationrelated to traveling of the human-powered vehicle from the sensor 60.The device controller 116 is configured to control, according to thedevice control program 10P, the device 30 with control data decidedbased on output information obtained if the acquired input informationis input to a first learning model 11M to be described later. The devicecontroller 116 is configured to control actions of a control object thatis mounted on the human-powered vehicle 1 as well as power supply to andcommunication with the control object based on the decided control dataaccording to the device control program 10P.

The interpolation processor 118 is configured to execute processing ofinterpolating the first learning model 11M stored in the memory 112using a second learning model trained with input information of ahuman-powered vehicle different in at least one of the human-poweredvehicle 1 and the rider of the human-powered vehicle 1, according to aninterpolation processing program 12P.

The electronic controller 110 is configured to execute processing undera different control state between a mode of automatic control of thedevice 30 by the function of the device controller 116 using the firstlearning model 11M to be described later and a learning mode of thefirst learning model 11M. The electronic controller 110 basicallyperforms the processing of the learning mode based on an operationperformed on the operation device 40 until the accuracy of the firstlearning model 11M reaches a certain level. When the accuracy of thefirst learning model 11M reaches the certain level, the electroniccontroller 110 interpolates the first learning model 11M by theinterpolation processor 118 while basically performing the processing ofthe automatic control mode using the first learning model 11M.

Here, the memory 112 includes a non-volatile memory such as a flashmemory, for example. The memory 112 stores the device control program10P and the interpolation processing program 12P. The device controlprogram 10P and the interpolation processing program 12P can be acquiredby the electronic controller 110 reading out a device control program90P and an interpolation processing program 92P stored in anon-transitory recording medium 900 and copying it onto the memory 112.

The memory 112 stores the first learning model 11M. The details of thefirst learning model 11M will be described below. The first learningmodel 11M can also be acquired by the electronic controller 110 readingout a first learning model 91M stored in the non-transitory recordingmedium 900 and copying it onto the memory 112.

The electronic controller 110 (including the device controller 116 andthe interpolation processor 118) communicates with an object to becontrolled. In this case, the electronic controller 110 can have its owncommunication device (not illustrated) intended for the control object,or the electronic controller 110 can be connected to a communicationdevice intended for the control object provided inside the controldevice 10. The electronic controller 110 preferably has a connectiondevice for communicating with the control object or the communicationdevice.

The electronic controller 110 preferably communicates with the controlobject by at least one of the PLC communication and the CANcommunication. Not limited to a wired communication, the communicationwith the control object performed by the electronic controller 110 canbe a wireless communication such as ANT®, ANT+®, Bluetooth®, WiFi®,ZigBee®, or the like.

The electronic controller 110 is connected to the sensor 60 through asignal line. The electronic controller 110 acquires input informationrelated to traveling of the human-powered vehicle 1 from a signal outputby the sensor 60 through the signal line.

The electronic controller 110 can communicate with an informationprocessing device 8 to be described later via a wireless communicationdevice 114 having an antenna. The term “wireless communication device”as used herein refers to a hardware device capable of wirelesslytransmitting and/or receiving a signal, and does not include a humanbeing. The wireless communication device 114 can be integrated into thecontrol device 100. The wireless communication device 114 is a devicethat implements communication over the so-called Internet. The wirelesscommunication device 114 can be a device used for wireless communicationsuch as ANT®, ANT+®, Bluetooth®, WiFi®, ZigBee®, Long Term Evolution(LTE), or the like. The wireless communication device 114 can becompliant with a communication network such as 3G, 4G, 5G, a Long TermEvolution (LTE), a Wide Area Network (WAN), a Local Area Network (LAN),an Internet line, a leased line, a satellite channel or the like.

The details of control performed by the control device 100 thusconfigured will be described. The electronic controller 110 of thecontrol device 100 automatically controls the device 30 according to thedevice control program 10P by the function of the device controller 116using control data decided based on output information obtained if theacquired input information is input to the first learning model 11M tobe described later. In the first embodiment, the electronic controller110 automatically controls the transmission device 31 by the devicecontroller 116 based on the information obtained if the inputinformation is input to the first learning model 11M.

FIG. 3 illustrates one example of the first learning model 11M. Thefirst learning model 11M is a learning model trained by supervised deeplearning using a Neural Network (hereinafter referred to as NN). Thefirst learning model 11M can be a model trained by a Recurrent NeuralNetwork (RNN). The first learning model 11M illustrated in FIG. 3 istrained so as to reproduce a gear ratio instructed to the transmissiondevice 31 in the case where input information related to traveling ofthe human-powered vehicle 1 acquired from the sensor 60 is input.

The first learning model 11M includes an input layer 111 to which inputinformation related to traveling of the human-powered vehicle 1 that isacquired from the sensor 60 is input. The first learning model 11Mincludes an output layer 115 from which output information related tocontrol of the transmission device 31 of the human-powered vehicle 1 isoutput. The first learning model 11M includes an intermediate layer 113that is located between the input layer 111 and the output layer 115, isformed by one or more layers each including a group of nodes, and istrained based on training data including an operation type accepted bythe operation device 40 related to the output information. The nodes inthe intermediate layer 113 each have a parameter including at least oneof the weight and the bias associated with the node in the previouslayer. The electronic controller 110 creates training data by labelingthe corresponding input information with a gear ratio actuallyinstructed to the transmission device 31 based on a part of the learningfunction of the interpolation processor 118. The electronic controller110 inputs the created training data to the input layer 111 and trainsparameters in the intermediate layer 113 so as to reduce the errorbetween the gear ratio output from the output layer 115 and the gearratio actually instructed by the rider. Thus, the first learning model11M is trained so as to reproduce a gear ratio to be instructed to thetransmission device 31 by the rider depending on a situation such as thespeed and acceleration of the human-powered vehicle 1 and the type of aroad, in response to the input information acquired from the sensor 60.

FIG. 4 illustrates another example of the first learning model 11M. Thefirst learning model 11M illustrated in FIG. 4 is a learning modeltrained by supervised deep learning using an NN as in the exampleillustrated in FIG. 3 . The first learning model 11M is trained so as tooutput the probability of the rider providing an instruction to thetransmission device 31 if input information related to the traveling ofthe human-powered vehicle 1 acquired from the sensor 60 is input.

The first learning model 11M includes an input layer 111, anintermediate layer 113 and an output layer 115. The first learning model11M as another example creates training data by labeling the inputinformation with whether or not change of the gear ratio is instructedto the transmission device 31. The electronic controller 110 inputs thecreated training data to the input layer 111 and trains the parametersin the intermediate layer 113 so as to reduce the error between theprobability output from the output layer 115 and the result of whetheror not change of the gear ratio is actually instructed. Thus, the firstlearning model 11M is trained so as to output the probability ofproviding an instruction to the transmission device 31 depending on thesituation such as a speed or acceleration of the human-powered vehicle 1or a road type, in response to the input information acquired from thesensor 60.

The device controller 116 of the human-powered vehicle 1 controls thetransmission device 31 using the output information output from thefirst learning model 11M. In the case where the first learning model 11Millustrated in FIG. 3 is used, the electronic controller 110 can changegears with the transmission device 31 based on the gear ratio outputfrom the first learning model 11M. In the case where the first learningmodel 11M illustrated in FIG. 4 is used, the electronic controller 110can change the gear ratio of the transmission device 31 based on theprobability output from the first learning model 11M.

Before shipment of the human-powered vehicle 1, the first learning model11M has been trained by using general-purpose training data so as tooutput the output information as illustrated in FIG. 3 or FIG. 4 inresponse to the input information. After shipment of the human-poweredvehicle 1, the control device 100 operates in the learning mode to allowthe electronic controller 110 to advance the learning of the firstlearning model 11M according to the preferences and characteristics ofthe rider. Such learning is however insufficient that optimizes thecontrol data with respect to the transmission device 31 for the rider inan environment where the human-powered vehicle 1 is not travelling. Inthe first embodiment, the control device 100 thus interpolates the firstlearning model 11M by the second learning model trained with inputinformation for another human-powered vehicle different in at least oneof the human-powered vehicle 1 and the rider of the human-poweredvehicle 1.

The second learning model used for interpolation is a learning modeltrained for another human-powered vehicle 1 or another rider, andcollected from the control device 100 corresponding to eachhuman-powered vehicle 1 to the information processing device 8.

FIG. 5 illustrates the control device 100 and the information processingdevice 8 according to the first embodiment. The control device 100according to the first embodiment is communicable with the informationprocessing device 8 through a communication network N as illustrated inFIG. 5 . The communication network N is composed of a communication linesuch as 3G, 4G, 5G, a Long Term Evolution (LTE), a Wide Area Network(WAN), a Local Area Network (LAN), an Internet line, a leased line, asatellite channel and communication equipment such as a base station.The control device 100 can employ an information terminal device 7configured to be communicable with the information processing device 8through the communication network N. The information terminal device 7is, for example, a smartphone, a cycle computer or the like used by therider of the human-powered vehicle 1. It can also function as a userinterface for receiving an input of instructions from the rider oroutputting information to the rider.

The information processing device 8 includes a memory 802 storingmultiple second learning models 82M. The control device 100 can use anyone of the multiple second learning models 82M through communicationwith the information processing device 8.

FIG. 6 is a block diagram illustrating the configuration of theinformation processing device 8. The information processing device 8 isprovided with an electronic controller 800, a memory 802 and acommunication device 804.

The electronic controller 800 is at least one processor employing a CPU.The electronic controller 800 can employ a graphics processing unit(GPU). The electronic controller 800 can employ a CPU and a GPU. Theelectronic controller 800 can include a memory such as a built-in ROM orRAM or the like and transmits and receives data to and from the controldevice 100.

The electronic controller 800 can be one or more processing circuitsincluding a Field Programmable Gate Array (FPGA), a Digital SignalProcessor (DSP), a quantum processor, a volatile or non-volatile memoryand the like.

The memory 802 is a bulk non-volatile memory such as a hard disk, asolid state drive (SSD) or the like. The memory 802 stores multiplesecond learning models 82M collected from respective human-poweredvehicles 1 by associating the second learning models 82M with the modelidentification data that identifies the second learning models 82M fromone another.

The memory 802 stores the model identification data of the secondlearning model 82M in association with the data that identifies at leastone of the trained human-powered vehicle 1 and the rider of the trainedhuman-powered vehicle 1. The memory 802 includes a model database 822that stores the specification and type of the rider of the human-poweredvehicle 1 in association with data that identifies at least one of thehuman-powered vehicle 1 and the rider. Thus, the electronic controller800 can determine the input information for which the second learningmodel 82M is trained.

The communication device 804 is a communication device that communicateswith the control device 100 through the network N. The term“communication device” as used herein refers to a hardware devicecapable of transmitting and/or receiving a signal, and does not includea human being. The communication device 804 adheres to the communicationnetwork such as 3G, 4G, 5G, an LTE, a WAN, a LAN, an Internet line, aleased line, a satellite channel or the like. The electronic controller800 transmits and receives data to and from the control device 100 viathe communication device 804.

With the control device 100 and the information processor 8 thusconfigured, the control device 100 controls the transmission device 31using the first learning model 11M interpolated by the second learningmodel 82M.

FIG. 7 is a flowchart showing one example of a learning method of thefirst learning model 11M according to the first embodiment. The controldevice 100 executes the following processing in the learning mode.

The electronic controller 110 of the control device 100 acquires inputinformation related to traveling of the human-powered vehicle 1 from thesensor 60 (step S101). The electronic controller 110 performs theprocessing at step S101 at multiple time points during the traveling.

At step S101, the electronic controller 110 acquires data from at leastone of the speed sensor 61, the acceleration sensor 62, the torquesensor 63, the cadence sensor 64, the gyro sensor 65, the seating sensor66 and the camera 67.

The electronic controller 110 inputs the acquired input informationrelated to traveling of the human-powered vehicle 1 to the firstlearning model 11M (step S103) and obtains output information outputfrom the first learning model 11M (step S105).

The electronic controller 110 determines whether or not the situation isan unlearned situation based on the comparison between the outputinformation obtained at step S105 and the operation type actuallyperformed on the transmission designating member(s) 40B by the rider inaccordance with the input information acquired at step S101 (step S107).

If the situation is determined to be an unlearned situation (S107: YES),the electronic controller 110 transmits a request for a second learningmodel 82M to the information processing device 8 by the wirelesscommunication device 114 (step S109).

When the information processing device 8 receives the request for asecond learning model 82M (step S801), the electronic controller 800specifies the identification data of the human-powered vehicle 1 withthe control device 100 having made a request (step S803).

From the multiple second learning models 82M stored in the informationprocessing device 8, the electronic controller 800 extracts the secondlearning model 82M corresponding to a human-powered vehicle or a ridersimilar to at least one of the human-powered vehicle with the controldevice 100 having made the request and the rider of the human-poweredvehicle (step S805).

As a first example, at step S805, the electronic controller 800 extractsthe second learning model 82M, from the multiple second learning models82M, that is trained in another human-powered vehicle 1 which is thesame as or similar to, in at least one of the type and size, thehuman-powered vehicle 1 with the control device 100 having made therequest. In the case where the type of the human-powered vehicle 1mounted with the control device 100 having made the request is amountain bike, for example, the electronic controller 800 extracts thesecond learning model 82 associated with the identification data ofanother human-powered vehicle 1 that is a mountain bike with referenceto the model database 822.

As a second example, at step S805, the electronic controller 800extracts the second learning model 82, from the multiple second learningmodels 82M that is trained in another human-powered vehicle 1 mountedwith a device 30 which is the same as or similar to, in at least one ofthe type and manufacturer, the device 30 mounted on the human-poweredvehicle 1 with the control device 100 having made the request. Thedevice 30 can be at least one of the suspension 33, the seat post 35,the brake device 37 and the assist device 30 other than the transmissiondevice 31. Referring to the model database 822, the electroniccontroller 800 extracts the second learning model 82M trained in ahuman-powered vehicle 1 which is the same or similar in at least one ofthe transmission device 31, the suspension 33, the seat post 35, thebrake device 37 and the assist device 39.

As a third example, at step S805, the electronic controller 800 extractsthe second learning model 82, from the multiple second learning models82M, that is trained in a human-powered vehicle 1 of the rider of thesame type as or a similar type to the rider of the human-powered vehicle1 with the control device 100 having made the request. Referring to themodel database 822, the electronic controller 800 extracts the secondlearning model 82M trained in a human-powered vehicle 1 ridden by therider of a similar type based on the identification data of the ridercategorized as a high cadence and high torque type, a high cadence andlow torque type, a low cadence and high torque type and a low cadenceand low torque type.

The electronic controller 800 can extract the multiple second learningmodels 82M using one of the methods in the first to third examples, orcan extract the multiple second learning models 82M using a combinationof two or three methods.

At step S805, the electronic controller 800 can sort the extractedmultiple second learning models 82M by similarity to narrow down to apredetermined number of second learning models 82M. The similarity is socalculated, for example, that the more items are identical, the higherthe similarity is.

The electronic controller 800 transmits the extracted candidates for thesecond learning model 82M to the control device 100 through thecommunication device 804 (step S807).

The electronic controller 110 receives from the information processingdevice 8 the multiple candidates for the second learning model 82Mtrained with the input information for another human-powered vehicle 1different in at least one of the human-powered vehicle 1 and the rider(step S111). The electronic controller 110 selects a second learningmodel 82M from the multiple candidates (step S113).

As a first example, at step S113, the electronic controller 110 selects,among the multiple candidates, the second learning model 82 for whichoutput information output in the case where the input informationacquired at step S101 is input is most similar to the operation typeactually performed on the transmission designating member(s) 40B by therider.

As a second example, at step S113, the electronic controller 110selects, among the multiple candidates, the second learning model 82similar in control data decided by the device controller 116 among theoutput information obtained if the same input information is input tothe multiple candidates for the second learning models 82. Morespecifically, the electronic controller 110 inputs the input informationacquired at step S101 to the candidates for the second learning model82M. The electronic controller 110 decides the control data which ismost similar in the operation type actually performed on thetransmission designating member(s) 40B by the rider among the controldata decided by the device controller 116 based on the outputinformation from the candidates for the second learning models 82M. Theelectronic controller 110 selects the second learning model 82 thatoutputs the output information that is the source of the control data.

As a third example, at step S113, the electronic controller 110 canselect any one of the received multiple candidates.

The electronic controller 110 executes interpolation processing byupdating at least part of the first learning model 11M with the secondlearning model 82M selected at step S113 (step S115). At step S115, theelectronic controller 110 can replace the entire first learning model11M with the selected second learning model 82M. At step S115, theelectronic controller 110 can update part of the parameters of the firstlearning model 11M with the parameters of the selected second learningmodel 82M.

The electronic controller 110 retrains the interpolated first learningmodel 11M with the training data labeling the input information acquiredat step S101 with the operation type actually performed on thetransmission designating member(s) 40B by the rider (step S117) and endsthe processing.

If determining that the situation is not an unlearned situation at stepS107 (S107: NO), the electronic controller 110 transmits the trainedfirst learning model 11M to the information processing device 8 throughthe wireless communication device 114 (step S119).

The information processing device 8 receives the trained first learningmodel 11M (step S809), stores it as the second learning model 82M in thememory 802 (step S811) and ends the processing.

The processing procedure of the flowchart in FIG. 7 can be executed atthe end of each traveling based on the input information stored at stepS101, not limited to during traveling of the human-powered vehicle 1.The first learning model 11M, which is determined to be untrainedaccording to the processing procedure of the flowchart in FIG. 7 , isinterpolated by the second learning model 82M. This lowers theprobability of being determined to be an unlearned situation at nextstep S107, and allows the use of the first learning model 11M for theautomatic control even in the unlearned situation.

FIG. 8 is a flowchart showing one example of the control processingusing the first learning model 11M according to the first embodiment.The control device 100 repeatedly executes the following processing inthe control mode.

The electronic controller 110 of the control device 100 acquires inputinformation related to traveling of the human-powered vehicle 1 from thesensor 60 (step S201).

The electronic controller 110 inputs the acquired input informationrelated to traveling of the human-powered vehicle 1 to the firstlearning model 11M (step S203) and obtains output information outputfrom the first learning model 11M (step S205).

The electronic controller 110 decides control data of the transmissiondevice 31 by the device controller 116 based on the output informationobtained at step S205 (step S207). At step S207, the electroniccontroller 110 can decide the gear ratio itself or decide whether tochange gears or not.

The electronic controller 110 controls the transmission device 31 withthe decided control data (step S209) and ends the processing. Theelectronic controller 110 repeatedly performs the processing at stepsS201 to S209.

According to the processing by the control device 100 of the firstembodiment, training of the first learning model 11M can be advancedusing the second learning model 82M even for the unlearned travelingsituation, which reduces the time required for training. The processingperformed by the electronic controller 110 allows interpolation of thefirst learning model 11M using the second learning model 82M even forthe unlearned traveling situations, so that automatic control can beachieved even in the unlearned situation.

Second Embodiment

In a second embodiment, the suspension 33, the seat post 35, the brakedevice 37 and the assist device 39 other than the transmission device 31are also assumed as objects to be controlled. In the second embodiment,an object to be controlled based on the first learning model 11M can beat least one of the transmission device 31, the suspension 33, the seatpost 35, the brake device 37 and the assist device 39.

In the second embodiment, the first learning model 11M is separatelytrained for each control object. FIG. 9 is a block diagram illustratingthe configuration of the control device 100 according to the secondembodiment. FIG. 9 illustrates the configuration of only the controldevice 100 and does not illustrate the device 30, the operation device40 and the sensor 60 that are connected to the control device 100.

The memory 112 of the control device 100 stores a first learning model11M used for control of the transmission device 31 and a first learningmodel 13M used for control of the suspension 33. The memory 112 alsostores a first learning model 15M used for control of the seat post 35,a first learning model 17M used for control of the brake device 37 and afirst learning model 19M used for control of the assist device 39.

The first learning models 11M, 13M, 15M, 17M and 19M can respectively becopied from first learning models 91M, 93M, 95M 97M and 99M stored in anon-transitory recording medium 900.

The electronic controller 110 of the control device 10 according to thesecond embodiment performs for each device 30 the processing proceduresshown in the flowcharts of FIGS. 8 and 10 as performed on thetransmission device 31 as a target in the first embodiment.

Thus, the control device 100 advances the learning of the first learningmodels 11M, 13M, 15M, 17M and 19M by means of the second learning model82M regarding at least one of the suspension 33, the seat post 35, thebrake device 37 and the assist device 39 as a target, respectively, evenfor an unlearned traveling situation. Thus, the time required forlearning can be shortened for any device 30.

Third Embodiment

In the third embodiment, the learning method employed in the case wherethe unlearned situation is determined is different. The configuration ofthe control device 100 according to the third embodiment is similar tothat of the first embodiment except for the details of the processingprocedure of the learning method to be described below. The parts commonto the first embodiment in the configuration of the control device 100according to the third embodiment are denoted by similar reference codesand detailed description thereof will not be repeated.

FIG. 10 is a flowchart showing one example of a learning method of thefirst learning model 11M according to the third embodiment. Theelectronic controller 110 executes the processing procedure of theflowchart in FIG. 7 demonstrated in the first embodiment. The electroniccontroller 110 receives the multiple second learning models 82M (S111),selects any one of them (step S113), and interpolates the first learningmodel 11M as described below.

The electronic controller 110 stores the selected one of the secondlearning models 82M in the memory 112 (step S501). The electroniccontroller 110 inputs the input information that is acquired and storedat step S101 to the selected second learning model 82M (step S503). Theelectronic controller 110 acquires output information output from thesecond learning model 82M (step S505).

The electronic controller 110 stores the input information associatedwith the output information as training data (step S507). The electroniccontroller 110 trains the first learning model 11M using the storedtraining data (step S509) and ends the learning processing.

In the third embodiment, the control device 100 trains the firstlearning model 11M using as training data the input information acquiredby the electronic controller 110 and the output information that isoutput in the case here the input information is input to the secondlearning model 82M as demonstrated in FIG. 10 .

Even in the situation where learning is insufficient in the controldevice 100 of the human-powered vehicle 1, control can be made using thetrained second learning model 82M, and training data created using thesecond learning model 82M can be used for training the first learningmodel 11M. This makes it possible to shorten the time required fortraining the unlearned situation.

Fourth Embodiment

The configuration of the control device 100 according to the fourthembodiment is similar to that of the control device 100 according to thefirst embodiment except for the configuration of the first learningmodel 11M and the details of the processing procedure using the firstlearning model 11M. The parts common to the first embodiment in theconfiguration of the control device 100 according to the fourthembodiment are denoted by similar reference codes and detaileddescription thereof will not be repeated.

FIG. 11 is a schematic diagram of the first learning model 11M accordingto the fourth embodiment. The first learning model 11M according to thefourth embodiment is also a learning model trained by supervised deeplearning using an NN. The first learning model 11M according to thefourth embodiment is trained with input information acquired in multipletraveling situations different from each other. The traveling situationis distinguished by at least one of on-road, off-road and urban districtsituations. The traveling situation is distinguished by at least one ofuphill, flat and downhill situations. In the following descriptions, thetraveling situation is distinguished by nine traveling situations ofbeing on-road and uphill, of being on-road and flat, of being on-roadand downhill, of being off-road and uphill, of being off-road and flat,of being off-road and downhill, of being in an urban district anduphill, of being in an urban district and flat and being in an urbandistrict and downhill. The traveling situation is not limited to beingdistinguished by nine. The traveling situation is not limited to theabove distinctions. The traveling situation can be distinguished by aspeed up or down related traveling situation such as when starting, whenaccelerated, when decelerated, and when stopped. The traveling situationcan be distinguished by a road shape related traveling situation, forexample, when traveling straight ahead, when entering a curve, whenexiting a curve, when entering an intersection, and when entering a roadwith reduced width. The traveling situation can be distinguished by ananother vehicle related traveling situation, for example, when findingno vehicle around, when approached by a car from behind, when travelingtogether with another human-powered vehicle and when passing on anotherhuman-powered vehicle.

The first learning model 11M includes an input layer 111 to which inputinformation related to traveling of the human-powered vehicle 1 acquiredfrom the sensor 60 and data indicating the situation are input. The restof the configuration is similar to that of the first learning model 11Mdescribed in the first embodiment. In the following description, thefirst learning model 11M is trained so as to reproduce a gear ratio tobe instructed to the transmission device 31 as one of the device 30 ifinput information related to traveling of the human-powered vehicle 1acquired from the sensor 60 and data indicating the traveling situationare input.

In the fourth embodiment, the electronic controller 110 creates trainingdata by labeling the corresponding input information with the gear ratioactually instructed to the transmission device 31 based on a part of thelearning function of the interpolation processor 118. The electroniccontroller 110 trains the parameters in the intermediate layer 113 so asto reduce an error between the gear ratio output from the output layer115 if the created training data and the traveling situation identifiedbased on the information acquired from the sensor 60 are input to theinput layer 111 and the gear ratio actually instructed by the rider.Thus, the first learning model 11M can be trained so as to reproduce, inresponse to the input information acquired from the sensor 60, a gearratio to be instructed to the transmission device 31 by the riderdepending on the traveling situation of the human-powered vehicle 1 andthe speed, acceleration and the like in this traveling situation.

The first learning model 11M in FIG. 11 outputs a numerical value of thecontrol data of the transmission device 31 as output information forcontrol of the device 30. In the fourth embodiment as well, the outputlayer 115 of the first learning model 11M can output the probability ofprovision of an instruction to the transmission device 31. In this case,the intermediate layer 113 is trained so as to output the probability ofthe rider instructing the transmission device 31 to change gearsdepending on the traveling situation.

In the fourth embodiment, the first learning models 11M, which aretrained in each control device 100, are also collected and stored in theinformation processing device 8.

FIGS. 12 and 13 are each a flowchart showing one example of a learningmethod of the first learning model 11M according to the fourthembodiment. The control device 100 executes the following processing inthe learning mode.

The electronic controller 110 of the control device 100 acquires inputinformation related to traveling of the human-powered vehicle 1 from thesensor 60 and stores the information (step S131). At step S131, theelectronic controller 110 acquires data from at least one of the speedsensor 61, the acceleration sensor 62, the torque sensor 63, the cadencesensor 64, the gyro sensor 65, the seating sensor 66 and the camera 67.

The electronic controller 110 judges the traveling situation based onthe input information acquired from the sensor 60 (S133).

At step S133, the electronic controller 110 judges the travelingsituation based on the information acquired from at least one of thespeed sensor 61, the acceleration sensor 62, the torque sensor 63, thecadence sensor 64, the gyro sensor 65, the seating sensor 66, the camera67 and the position information sensor 68. As a first example, theelectronic controller 110 judges whether or not the road on which thehuman-powered vehicle 1 is traveling is off-road, an urban district oron-road based on the information related to the position of thehuman-powered vehicle 1 acquired from the position information sensor 68and the map information possessed in advance by the human-poweredvehicle 1. In the first example, the electronic controller 110 can beconfigured to acquire map information from the outside by communication.As a second example, the electronic controller 110 judges whether or notthe road on which the human-powered vehicle 1 is traveling is off-road,an urban district or on-road based on the information related to thevibration of the human-powered vehicle 1 acquired from the accelerationsensor 62 and the attitude of the human-powered vehicle 1 acquired fromthe gyro sensor 65. In the second example, the electronic controller 110can identify the traveling situation as being off-road if the frequencyof vibration greater than a predetermined value is higher than apredetermined frequency. As a third example, the electronic controller110 can identify the traveling situation as being off-road if thefrequency of traveling in an unseated state is higher than apredetermined frequency. As a fourth example, the electronic controller110 can identify the traveling situation as being an urban district ifthe number of repetitions of stops and starts is greater than apredetermined number of times relative to the travel distance. As afifth example, the electronic controller 110 can identify the travelingsituation as being on-road if the torque and cadence are constant. As asixth example, the electronic controller 110 can identify the travelingsituation as being on-road if the situation is neither off-road nor anurban district.

At step S133, the electronic controller 110 can identify whether or notthe traveling situation is uphill, downhill or flat depending on thetilt in the pitch direction of the human-powered vehicle 1 by the gyrosensor 65.

The electronic controller 110 determines whether or not the travelingsituation judged at step S133 is an unlearned traveling situation (stepS135). At step S135, the electronic controller 110 determines whether ornot the traveling situation is an unlearned traveling situation based onthe information indicating the learned situation that is stored by beingassociated with the data identifying the traveling situation in thememory 112.

If the traveling situation is determined as an unlearned travelingsituation (S135: YES), the electronic controller 110 transmits a requestfor a second learning model 82M to the information processing device 8through the wireless communication device 114 (step S137). At step S137,the electronic controller 110 transmits the request while designatingthe data for identifying the judged traveling situation.

When the information processing device 8 receives the request for asecond learning model 82M (step S821), the electronic controller 800specifies the identification data of the human-powered vehicle 1 withthe control device 100 having made the request (step S823).

The electronic controller 800 extracts the second learning models 82Mtrained for the traveling situation that is designated in the requestfrom the multiple second learning models 82 stored in the informationprocessing device 8 (step S825). Among the extracted second learningmodels 82M, the electronic controller 800 selects as candidates thesecond learning models 82M corresponding to a human-powered vehicle or arider similar to at least one of the human-powered vehicle with thecontrol device 100 having made a request and the rider of thehuman-powered vehicle (step S827).

At step S827, the electronic controller 800 can select at least one ofthe methods in the first to third examples described at step S805 in theprocessing procedure of the flowchart in FIG. 7 according to the firstembodiment.

The electronic controller 800 transmits the selected candidates for thesecond learning models 82M to the control device 100 through thecommunication device 804 (step S829).

The electronic controller 110 receives from the information processingdevice 8 the multiple candidates for the second learning models 82Mtrained with the input information for other human-powered vehicles 1different in at least one of the human-powered vehicle 1 and the rider(step S139). The electronic controller 110 selects a second learningmodel 82M from the multiple candidates (step S141).

At step S141, as a first example, the electronic controller 110 selects,among the multiple candidates, the second learning model 82M for whichoutput information output if the input information acquired at step S101is input is most similar to the operation type actually performed on thetransmission designating member(s) 40B by the rider.

At step S141, as a second example, the electronic controller 110selects, among the multiple candidates, the second learning model 82Mfor which the output information output if input information acquired inanother traveling situation having been trained by the first learningmodel 11M and stored is input is similar to the output informationoutput if input information is input to the first learning model 11M.

At step S141, as a third example, the electronic controller 110 selects,from the multiple candidates, the second learning model 82M for whichthe control data decided by the device controller 116 based on theoutput information obtained if the same input information is input issimilar. More specifically, the electronic controller 110 inputs theinput information acquired at step S131 to the candidates for the secondlearning models 82M. The electronic controller 110 decides, out of thecontrol data decided by the device controller 116 based on the outputinformation output from the second learning models 82M, control datawhich is most similar to the operation type actually performed on thetransmission designating member(s) 40B by the rider. The electroniccontroller 110 selects the second learning model 82 that outputs outputinformation that is the source of the decided control data.

At step S141, as a fourth example, the electronic controller 110employs, among the multiple candidates, the second learning model 82Mfor which the control data decided by the device controller 116 based onthe output information obtained if the input information acquired inanother learned traveling situation is input is similar. Morespecifically, the electronic controller 110 inputs input informationthat is acquired and stored in another learned traveling situation anddata identifying its traveling situation to candidates for the secondlearning model 82M. The electronic controller 110 inputs the storedinput information and the data for identifying the traveling situationto the trained first learning model 11M. The electronic controller 110acquires control data decided by the device controller 116 based on theoutput information obtained from the first learning model 11M in thetrained traveling situation. This control data is not used for control.The electronic controller 110 acquires control data decided by thedevice controller 116 based on the output information output from thecandidates for the second learning model 82M. The electronic controller110 selects, among the candidates for the second learning models 82, thesecond learning model 82M that outputs the output information that isthe source of the control data similar to the control data decided basedon the output information from the first learning model 11M.

At step S141, as a fifth example, the electronic controller 110 canselect any one of the received multiple candidates.

The electronic controller 110 performs the interpolation processing byupdating at least part of the first learning model 11M with the secondlearning model 82M selected at step S141 (step S143). At step S143, theelectronic controller 110 performs training using the output informationobtained from the second learning model 82 as training data according tothe procedure described in the third embodiment. At step S143, theelectronic controller 110 can replace the entire first learning model11M with the selected second learning model 82M. At step S143, theelectronic controller 110 can update part of the parameters of the firstlearning model 11M with the parameters of the selected second learningmodel 82M.

The electronic controller 110 retrains the interpolated first learningmodel 11M with the training data labeling the input information acquiredat step S131 with the corresponding operation type actually instructedto the transmission designating member(s) 40B by the rider (step S145).The electronic controller 110 stores the learned situation in the memory112 in association with the data for identifying the traveling situationjudged at step S133 (step S147).

If determining that the traveling situation is not an unlearnedtraveling situation at step S135 (S135: NO), the electronic controller110 transmits the trained first learning model 11M together with thedata for identifying the traveling situation to the informationprocessing device 8 thorough the wireless communication device 114 (stepS149).

The information processing device 8 receives the trained first learningmodel 11M (step S831), stores it together with the data identifying thetraveling situation as the second learning model 82M in the memory 802(step S833) and ends the processing.

The processing procedure of the flowchart in FIGS. 12 and 13 can beexecuted at the end of each traveling based on the input informationstored at step S131, not limited to during traveling of thehuman-powered vehicle 1. With the processing procedure of the flowchartsin FIGS. 12 and 13 , the first learning model 11M is interpolated by thesecond learning model 82M for an unlearned traveling situation. Thislowers the probability of being determined to be an unlearned situationat next step S107, and allows the use of the first learning model 11Mfor the automatic control even in the unlearned situation.

FIG. 14 is a flowchart showing one example of control processing of thefirst learning model 11M according to the fourth embodiment. The controldevice 100 repeatedly executes the following processing in the controlmode.

The electronic controller 110 of the control device 100 acquires inputinformation related to traveling of the human-powered vehicle 1 from thesensor 60 (step S221). The electronic controller 110 judges thetraveling situation based on the input information acquired from thesensor 60 (step S223).

The electronic controller 110 inputs the acquired input informationrelated to traveling of the human-powered vehicle 1 and data foridentifying the judged traveling situation to the first learning model11M (step S225). The electronic controller 110 obtains the outputinformation output from the first learning model 11M (step S227).

The electronic controller 110 decides the control data of thetransmission device 31 by the device controller 116 based on the outputinformation obtained at step S227 (step S229). At step S229, theelectronic controller 110 can decide the gear ratio itself or decidewhether to change gears or not.

The electronic controller 110 controls the transmission device 31 withthe decided control data (step S231) and ends the processing. Theelectronic controller 110 repeatedly executes the processing at stepsS221 to 231.

If there is an intervention operation by the rider with the controldevice 10 after the control at step S231, the electronic controller 110restores that the target traveling situation is an unlearned situationand can perform the processing procedure of the flowcharts in FIGS. 12and 13 . In this case, the electronic controller 110 retrains the firstlearning model 11M interpolated by the second learning model 82M usingthe training data including the input information acquired from thesensor 60 of the human-powered vehicle 1, data for identifying thetraveling situation and the operation instructed from the rider.

According to the processing performed by the control device 100 of thefourth embodiment, the learning time can be shortened for the firstlearning model 11M from which output information related to control datacan appropriately be obtained, depending on whether the situation is anunlearned traveling situation or not.

Fifth Embodiment

The configuration of the control device 100 according to the fifthembodiment is similar to that of the control device 100 of the firstembodiment except for the configuration of the first learning model 11Mand the details of the processing procedure using the first learningmodel 11M. The parts common to the first embodiment in the configurationof the control device 100 according to the fifth embodiment are denotedby similar reference codes and detailed description thereof will not berepeated.

FIG. 15 illustrates a first learning model 11M according to the fifthembodiment. In the fifth embodiment, the first learning model 11Mincludes multiple learning models 11MA, 11MB . . . stored for eachtraveling situation. The learning models 11MA, 11MB, 11MC, . . . areeach a learning model that includes the input layer, the output layerand the intermediate layer as configured in FIG. 3 or 4 of the firstembodiment. The detailed description of the input layer, output layerand intermediate layer will not be repeated here.

The learning models 11MA, 11MB, 11MC, . . . are trained for eachtraveling situation so as to output the control data for the device 30if input information on the traveling of the human-powered vehicle 1acquired from the sensor 60 is input. As illustrated in FIG. 14 , in thefifth embodiment, the traveling situation is distinguished by at leastone of the on-road, off-road and urban district situations and isdistinguished by at least one of uphill, flat and downhill situations.In the following descriptions, the traveling situation is distinguishedby nine traveling situations of being on-road and uphill, of beingon-road and flat, of being on-road and downhill, of being off-road anduphill, of being off-road and flat, of being off-road and downhill, ofbeing in an urban district and uphill, of being in an urban district andflat and being in an urban district and downhill.

For example, the learning model 11MA is trained using training dataincluding input information acquired from the sensor 60 of thehuman-powered vehicle 1 that is traveling in the on-road and uphilltraveling situation and an operation type instructed by the rider. Thelearning model 11MB is trained using training data including inputinformation acquired from the sensor 60 of the human-powered vehicle 1that is traveling in the on-road and flat traveling situation and anoperation type instructed by the rider. Likewise, the learning model11MC is trained using training data including input information acquiredfrom the sensor 60 of the human-powered vehicle 1 that is traveling inthe on-road and downhill traveling situation and an operation typeinstructed by the rider. The learning models 11MD, 11ME, 11MF, 11MG,11MH and 11MI are respectively trained for the off-road and uphillsituation, the off-road and flat situation, the off-road and downhillsituation, the urban district and uphill situation, the urban districtand flat situation and the urban district and downhill situation.

The processing will be described below that is performed by the controldevice 100 using the first learning model 11M including multiplelearning models trained for each traveling situation as mentioned above.

FIGS. 16 and 17 are each a flowchart showing one example of a learningmethod of the first learning model 11M according to the fifthembodiment. Among the processing procedure described in the flowchart ofFIGS. 16 and 17 , procedures common to those described in the flowchartof FIGS. 12 and 13 in the fourth embodiment are denoted by similarreference codes and detailed description thereof will not be repeated.

The electronic controller 110 acquires and stores input information(S131) and judges the traveling situation (S133).

The electronic controller 110 determines whether or not the trainedfirst learning model 11M stored in the memory 112 includes the learningmodel 11MA, 11MB, . . . trained for the traveling situation identifiedat step S133 (step S151).

If it is determined that the first learning model 11M includes thelearning model 11MA, 11MB, . . . trained for the identified travelingsituation (S151: YES), the electronic controller 110 executes theprocessing at step S153. The electronic controller 110 transmits datafor identifying the judged traveling situation and the learning model11MA, 11MB, . . . trained for the judged traveling situation to theinformation processing device 8 thorough the wireless communicationdevice 114 (step S153).

The information processing device 8 receives the data identifying thejudged situation and the first learning model 11M trained for the judgedsituation (step S851). The electronic controller 800 stores the receivedfirst learning model 11M together with the data for identifying thetraveling situation as the second learning model 82M in the memory 802(step S853) and ends the processing. The memory 802 stores the firstlearning model 11M including the multiple learning models as the secondlearning model 82.

If it is determined that the first learning model 11M does not includethe learning model 11MA, 11MB, . . . trained for the judged travelingsituation (S151: NO), the electronic controller 110 transmits a requestfor the second learning model 82M to the information processing device 8through the wireless communication device 114 (step S155). At step S155,the electronic controller 110 transmits a request while designating thedata for identifying the judged traveling situation.

When the information processing device 8 receives the request for thesecond learning model 82M (step S855), the electronic controller 800specifies the identification data of the human-powered vehicle 1 withthe control device 100 having made the request (step S857).

The electronic controller 800 extracts the second learning models 82Meach including the trained learning model for the traveling situationdesignated in the request out of the multiple second learning models 82stored in the information processing device 8 (step S859). From theextracted second learning models 82M, the electronic controller 800selects as candidates the second learning models 82M corresponding to ahuman-powered vehicle or rider similar to at least one of thehuman-powered vehicle 1 with the control device 100 having made therequest and the rider of the human-powered vehicle (step S861).

At step S861, the electronic controller 800 can select at least one ofthe methods in the first to third examples described at step S805 in theprocessing procedure of the flowchart in FIG. 7 according to the firstembodiment.

The electronic controller 800 transmits the selected candidates for thesecond learning model 82M to the electronic controller 110 through thecommunication device 804 (step S863).

The electronic controller 110 receives from the information processingdevice 8 the multiple candidates for the second learning model 82Mtrained with the input information in another human-powered vehicle 1different in at least one of the human-powered vehicle 1 and the rider(step S157). The electronic controller 110 selects the second learningmodel 82M from the multiple candidates (step S159).

At step S159, among the multiple candidates, the electronic controller110 uses the second learning model 82M for which the output informationoutput if input information in the traveling situation for which thefirst learning model 11M has been trained is input to the learning modelused for this traveling situation is similar to the output informationoutput if input information is input to the learning model 11MA, 11MB .. . . More specifically, the electronic controller 110 selects any oneof the learning models 11MA, 11MB, . . . for another learned travelingsituation that is different from the traveling situation judged at stepS133 from the first learning model 11M. The electronic controller 110selects learning models for the same learned traveling situation fromthe candidates for the second learning model 82M. The electroniccontroller 110 stores output information that is output if the inputinformation in the learned traveling situation is input to the selectedone of the learning models, for example, the learning model 11MA. Theelectronic controller 110 stores output information that is output ifthe input information in the learned traveling situation is input to thelearning models selected from the candidates. The electronic controller110 selects, among the learning models selected from the candidates, thelearning model that outputs the output information similar to the outputinformation output from the selected one of the learning models, forexample, 11MA. The electronic controller 110 specifies the secondlearning model 82 as a candidate that includes the selected learningmodel.

At step S159, among the multiple candidates, the electronic controller110 uses the second learning model 82M for which control data decidedbased on the output information obtained if input information of thelearned traveling situation in the first learning model 11M is input issimilar to control data decided based on the output information obtainedif this input information is input to the first learning model 11M. Morespecifically, the electronic controller 110 selects any one of thelearning models 11MA, 11MB, . . . for another learned travelingsituation that is different from the traveling situation judged at stepS133 from the first learning model 11M. The electronic controller 110selects learning models in the same traveling situation as the selectedone of the learning models, for example, the learning model 11MA fromthe candidates for the second learning model 82M. The electroniccontroller 110 inputs the input information for the learned travelingsituation to the selected learning model 11MA. The electronic controller110 acquires control data decided by the device controller 116 based onthe output information obtained from the one of the learning models 11MAselected for the learned traveling situation. This control data is notused for control. The electronic controller 110 inputs the inputinformation for the learned traveling situation to the learning modelsselected from the candidates for the second learning model 82M that areused for the same traveling situation as the learning model 11MA. Theelectronic controller 110 acquires control data decided by the devicecontroller 116 based on the output information output from the learningmodels used for the same traveling situation as the leaning model 11MA.Among the candidates for the second learning model 82, the electroniccontroller 110 selects the second learning model 82M including thelearning model that outputs the output information that is the source ofthe control data similar to the control data decided based on the outputinformation output from the learning model 11MA.

The electronic controller 110 acquires the learning model correspondingto the unlearned traveling situation judged at step S133 that isdifferent from the learned traveling situation out of the multiplelearning models included in the selected second learning model 82 (stepS161).

The electronic controller 110 executes the interpolation processing bystoring the acquired learning model as a learning model for thetraveling situation judged at step S133 in the first learning model 11M(step S163) and ends the processing.

FIG. 18 illustrates processing performed by the interpolation processor118 according to the fifth embodiment. The first learning model 11M inFIG. 18 has originally already learned the learning models 11MA, 11MBand 11MC used for the three on-road related traveling situations out ofthe nine traveling situations. It has not learned the other travelingsituations. In FIG. 17 , the learning models 11MA, 11MB and 11MC for thelearned traveling situations are represented by solid lines, and thelearning models for the unlearned traveling situations are representedby dashed lines.

In the case where the human-powered vehicle 1 with the control device100 storing the first learning model 11M illustrated in FIG. 18 startstraveling in the off-road situation, the electronic controller 110judges the traveling situation as off-road by the inclination or thelike of the human-powered vehicle 1. The electronic controller 110judges that the first learning model 11M does not include the learningmodels trained for the off-road traveling situation (S151: YES). Theelectronic controller 110 makes a request to the information processingdevice 8 to acquire candidates for the second learning model 82Mincluding the learning models trained for the off-road travelingsituation. The electronic controller 110 selects the second learningmodel 82M that outputs the most similar output information for theon-road situation among the acquired candidates for the second learningmodel 82M. The electronic controller 110 acquires the learning modelsfor the off-road traveling situation included in the selected secondlearning model 82M. As illustrated in FIG. 17 , the first learning model11M is thus interpolated by the model including the learning models11MD, 11ME, and 11MF trained for the off-road traveling situation.

FIG. 19 is a flowchart showing one example of the control processingusing the first learning model according to the fifth embodiment. Thecontrol device 100 repeatedly executes the following processing in thecontrol mode.

The electronic controller 110 of the control device 100 acquires inputinformation related to traveling of the human-powered vehicle 1 from thesensor 60 (step S251). The electronic controller 110 judges thetraveling situation based on the input information acquired from thesensor 60 (step S253).

The electronic controller 110 selects the learning model used for thejudged traveling situation from the first learning model 11M (stepS255). The electronic controller 110 inputs the acquired inputinformation related to traveling of the human-powered vehicle 1 to thelearning model selected at step S255 (step S257). The electroniccontroller 110 obtains output information output from the first learningmodel 11M (step S259).

The electronic controller 110 decides control data of the transmissiondevice 31 by the device controller 116 based on the output informationobtained at step S227 (step S261). At step S261, the electroniccontroller 110 can decide the gear ratio itself or decide whether toshift gears or not.

The electronic controller 110 controls the transmission device 31 by thedecided control data (step S263) and ends the processing. The electroniccontroller 110 repeatedly executes the processing at steps S251 to S263.

In the fifth embodiment, as described above, the first learning model11M is interpolated by another second learning model 82M for theunlearned traveling situation as well. This enables automatic controlusing the first learning model 11M interpolated by another secondlearning model 82M even for the unlearned traveling situation as well.

Sixth Embodiment

FIG. 20 illustrates the control device 100 and the informationprocessing device 8 according to the sixth embodiment. The configurationof the control device 100 according to the sixth embodiment is similarto that of the control device 100 according to the first embodiment. Thecontrol device 100 and the information processing device 8 according tothe sixth embodiment are similar to those of the first embodiment exceptfor the processing procedure to be described below in the informationprocessing device 8. The parts common to the first embodiment in theconfiguration of the control device 100 and the information processingdevice 8 according to the sixth embodiment are denoted by similarreference codes and detailed description thereof will not be repeated.

In the sixth embodiment, the second learning model 82 includes learningmodels for each traveling situation. In the sixth embodiment, theinformation processing device 8 creates each learning model byperforming statistic processing on parameters including at least one ofthe weights and biases of the learning models for each travelingsituation that are collected from the control devices 100.

FIG. 21 is a flowchart of one example of a processing procedure of theinformation processing device 8 according to the sixth embodiment. Theinformation processing device 8 executes the following processing whenreceiving the first learning model 11M sent from the control device 100.

When receiving the first learning model 11M (step S601), the electroniccontroller 800 specifies the identification data of the human-poweredvehicle 1 with the electronic controller 110 as a transmission source(step S603).

The electronic controller 800 extracts second learning models 82Mcorresponding to a human-powered vehicle or rider similar to at leastone of the human-powered vehicle 1 with the control device 100 havingmade a request and the rider of the human-powered vehicle 1 concerningthe received first learning model 11M (step S605). In the informationprocessing device 8, the second learning models 82M are stored for eachtype of the rider and each type of the human-powered vehicle 1.

At step S605, the electronic controller 800 extracts the second learningmodel 82M using at least one of the methods in the first to thirdexamples described at step S805 of the flowchart shown in FIG. 7according to the first embodiment.

The electronic controller 800 performs statistic processing onparameters including at least one of the weights and biases of thelearning models for each traveling situation included in the receivedfirst learning model 11M and the learning models for each travelingsituation included in the extracted second learning model 82M to createlearning model (step S607).

The electronic controller 800 updates the second learning model 82Mextracted at step S605 with the second learning model 82M including thecreated learning models (step S609), and ends the processing.

FIG. 22 is a schematic diagram of the second learning model 82 accordingto the sixth embodiment. In the sixth embodiment, as illustrated in FIG.22 , the second learning model 82 includes learning models for eachtraveling situation. As illustrated in FIG. 21 , the learning models bytraveling situation are each acquired by performing statistic processingon the parameters including at least one of the weights and biases ofthe multiple learning models for the same traveling situation that arecollected from the control device 10 of the human-powered vehicle 1 ofthe rider of the same type.

According to the processing by the information processing device 8 ofthe sixth embodiment, the second learning model 82M stored in theinformation processing device 8 is a model acquired by performingstatistic processing on models trained in multiple human-poweredvehicles 1 and adding up them. The control device 100 can thus performinterpolation using general-purpose second learning models 82M, notlearning models for a specific human-powered vehicle 1 or for a specificrider.

It is to be understood that the embodiments disclosed here areillustrative in all respects and not restrictive. The scope of thepresent invention is defined by the appended claims, not by theabove-mentioned meaning, and all changes that fall within the meaningsand the bounds of the claims, or equivalence of such meanings and boundsare intended to be embraced by the claims.

What is claimed is:
 1. A human-powered vehicle control devicecomprising: at least one sensor configured to acquire input informationrelated to traveling of a human-powered vehicle; a memory configured tostore a first learning model trained so as to output output informationrelated to control of a device mounted on the human-powered vehiclebased on the input information acquired; an electronic controllerconfigured to control the device by control data decided based on outputinformation obtained by inputting the input information to the firstlearning model; and an interpolation processor configured to executeprocessing of interpolating the first learning model stored in thememory using a second learning model trained with input information in ahuman-powered vehicle different in at least one of the human-poweredvehicle and a rider of the human-powered vehicle.
 2. The human-poweredvehicle control device according to claim 1, wherein the interpolationprocessor is configured to update at least part of the first learningmodel stored in the memory with the second learning model.
 3. Thehuman-powered vehicle control device according to claim 1, wherein theinterpolation processor is configured to train the first learning modelusing, as training data, input information acquired by the at least onesensor and output information output if the input information is inputto the second learning model.
 4. The human-powered vehicle controldevice according to claim 1, wherein the first learning model is trainedwith input information acquired by the at least one sensor in aplurality of traveling situations different from each other, and as to atraveling situation that is an unlearned traveling situation differentfrom a learned traveling situation learned by the first learning modeland that is learned by a second learning model, the interpolationprocessor is configured to interpolate the first learning model usingthe second learning model.
 5. The human-powered vehicle control deviceaccording to claim 1, wherein the first learning model includes aplurality of learning models stored for each traveling situation, andthe interpolation processor is configured to use, as a learning modelcorresponding to an unlearned traveling situation different from alearned traveling situation learned by the first learning model out ofthe plurality of learning models, a part of the second learning modelthat has already learned the unlearned traveling situation.
 6. Thehuman-powered vehicle control device according to claim 4, wherein thetraveling situation is distinguished by at least one of on-road,off-road and urban district situations.
 7. The human-powered vehiclecontrol device according to claim 4, wherein the traveling situation isdistinguished by at least one of uphill, flat and downhill situations.8. The human-powered vehicle control device according to claim 1,wherein the interpolation processor is configured to use the secondlearning model for which output information output if the same inputinformation is input is similar to output information output if theinput information is input to the first learning model.
 9. Thehuman-powered vehicle control device according to claim 8, wherein thefirst learning model is trained with input information acquired by theat least one sensor for a plurality of traveling situations differentfrom each other, and out of a plurality of the second learning models,the interpolation processor is configured to use the second learningmodel for which output information output if input information in atraveling situation already learned by the first learning model is inputis similar to output information output if the input information isinput to the first learning model.
 10. The human-powered vehicle controldevice according to claim 8, wherein the first learning model and thesecond learning model each include a plurality of learning models storedfor each traveling situation, the interpolation processor is configuredto use, out of a plurality of the second learning models, the secondlearning model for which output information output if input informationin a traveling situation already learned by the first learning model isinput is similar to output information output if the input informationis input to the first learning model, and acquire, out of the pluralityof learning models included in the second learning model, the learningmodel that corresponds to an unlearned traveling situation differentfrom the learned traveling situation.
 11. The human-powered vehiclecontrol device according to claim 1, wherein the interpolation processoris configured to use a second learning model used in anotherhuman-powered vehicle control device similar in control data decidedbased on the same input information.
 12. The human-powered vehiclecontrol device according to claim 11, wherein the first learning modelis trained with input information acquired by the at least one sensor ina plurality of traveling situations different from each other, and theinterpolation processor is configured to use, out of a plurality of thesecond learning models, the second learning model for which control datadecided based on output information output if input information in atraveling situation already learned by the first learning model is inputis similar to control data decided based on output information output ifthe input information is input to the first learning model.
 13. Thehuman-powered vehicle control device according to claim 11, wherein thefirst learning model and the second learning model each include aplurality of learning models stored for each traveling situation, theinterpolation processor is configured to use, out of a plurality of thesecond learning models, the second learning model for which control datadecided based on output information output if input information in atraveling situation already learned by the first learning model is inputis similar to control data decided based on output information output ifthe input information is input to the first learning model, and acquire,out of the plurality of learning models included in the second learningmodel, the learning model corresponding to an unlearned travelingsituation different from the learned traveling situation.
 14. Thehuman-powered vehicle control device according to claim 1, wherein theinterpolation processor is configured to use, out of a plurality of thesecond learning models, the second learning model that is trained withinput information in another human-powered vehicle the same as orsimilar to, in at least one of type and size, the human-powered vehicle.15. The human-powered vehicle control device according to claim 1,wherein the interpolation processor is configured to use, out of aplurality of the second learning models, the second learning model thatis trained with input information of another human-powered vehiclemounted with a device having the same type and manufacture as or similartype and manufacturer to the device.
 16. The human-powered vehiclecontrol device according to claim 15, wherein the device isdistinguished by at least one type of a transmission device, asuspension, a seat post, a brake device and an assist device.
 17. Thehuman-powered vehicle control device according to claim 1, wherein theinterpolation processor is configured to use, out of a plurality of thesecond learning models, the second learning model that is trained withinput information for a human-powered vehicle of a rider of the sametype as or similar type to a rider of the human-powered vehicle.
 18. Thehuman-powered vehicle control device according to claim 1, wherein thefirst learning model stored in the memory is sent to another device. 19.The human-powered vehicle control device according to claim 1, whereinthe second learning model is a model acquired by performing staticprocessing on parameters including at least one of weights and biases ofa plurality of models trained in a plurality of other human-poweredvehicles.
 20. A learning method causing a computer mounted on ahuman-powered vehicle to execute processing of: selecting externally, asto a first learning model trained so as to output output informationrelated to control of a device mounted on the human-powered vehiclebased on input information related to traveling of the human-poweredvehicle, a second learning model trained with input information in ahuman-powered vehicle different in at least one of the human-poweredvehicle and a rider of the human-powered vehicle; and performinginterpolation processing of the first learning model using the secondlearning model selected.
 21. A human-powered vehicle control methodcausing a computer mounted on a human-powered vehicle to execute theprocessing of: selecting externally, as to a first learning modeltrained so as to output output information related to control of adevice mounted on the human-powered vehicle based on input informationrelated to traveling of the human-powered vehicle, a second learningmodel trained with input information in a human-powered vehicledifferent in at least one of the human-powered vehicle and a rider ofthe human-powered vehicle; performing interpolation processing of thefirst learning model using the second learning model selected; decidingcontrol data based on output information obtained by inputting the inputinformation to the first learning model interpolated; and controllingthe device with the control data decided.
 22. A computer programdisposed upon a non-transitory computer readable storage medium andexecutable by a computer mounted on a human-powered vehicle, thecomputer program being configured to cause the computer to execute theprocessing of: selecting externally, as to a first learning modeltrained so as to output output information related to control of adevice mounted on the human-powered vehicle based on input informationrelated to traveling of the human-powered vehicle, a second learningmodel trained with input information in a human-powered vehicledifferent in at least one of the human-powered vehicle and a rider ofthe human-powered vehicle; and performing interpolation processing ofthe first learning model using the second learning model selected.